





'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase									=   logging.get_logger(__name__)






def   __A    (				a_    :	Union[str, Any]						):
		lowerCAmelCase  :     Dict       						= SwinConfig(
		    embed_dim=1_9_2   ,depths=(2, 2, 1_8, 2)   ,num_heads=(6, 1_2, 2_4, 4_8)   ,window_size=1_2   ,out_features=["stage2", "stage3", "stage4"]   ,)

		lowerCAmelCase  :     Any       						= DetaConfig(
		    backbone_config=a_   ,num_queries=9_0_0   ,encoder_ffn_dim=2_0_4_8   ,decoder_ffn_dim=2_0_4_8   ,num_feature_levels=5   ,assign_first_stage=a_   ,with_box_refine=a_   ,two_stage=a_   ,)

		# set labels
		lowerCAmelCase  :     Optional[int]       						= "huggingface/label-files"
		if "o365" in model_name:
				lowerCAmelCase  :     List[str]       						= 3_6_6
				lowerCAmelCase  :     Any       						= "object365-id2label.json"
		else:
				lowerCAmelCase  :     Optional[Any]       						= 9_1
				lowerCAmelCase  :     Dict       						= "coco-detection-id2label.json"

		lowerCAmelCase  :     Tuple       						= num_labels
		lowerCAmelCase  :     List[Any]       						= json.load(open(cached_download(hf_hub_url(a_   ,a_   ,repo_type="dataset"						)						)   ,"r"						)						)
		lowerCAmelCase  :     List[str]       						= {int(a_						): v for k, v in idalabel.items()}
		lowerCAmelCase  :     List[str]       						= idalabel
		lowerCAmelCase  :     Union[str, Any]       						= {v: k for k, v in idalabel.items()}

		return config






def   __A    (				a_    :	Optional[Any]						):
		lowerCAmelCase  :     Union[str, Any]       						= []

		# stem
		# fmt: off
		rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")						)
		rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")						)
		rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")						)
		rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")						)
		# stages
		for i in range(len(config.backbone_config.depths						)						):
				for j in range(config.backbone_config.depths[i]						):
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')						)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')						)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')						)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')						)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')						)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')						)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')						)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')						)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')						)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')						)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')						)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')						)

				if i < 3:
						rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.reduction.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')						)
						rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')						)
						rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.bias''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')						)

		rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")						)
		rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")						)
		rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")						)
		rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")						)
		rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")						)
		rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")						)

		# transformer encoder
		for i in range(config.encoder_layers						):
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')						)
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')						)

		# transformer decoder
		for i in range(config.decoder_layers						):
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')						)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')						)

		# fmt: on

		return rename_keys






def   __A    (				a_    :	List[Any]   ,a_    :	int   ,a_    :	List[str]						):
		lowerCAmelCase  :     Optional[int]       						= dct.pop(a_						)
		lowerCAmelCase  :     Union[str, Any]       						= val






def   __A    (				a_    :	Tuple   ,a_    :	List[str]						):
		lowerCAmelCase  :     str       						= [int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
		for i in range(len(backbone_config.depths						)						):
				lowerCAmelCase  :     Tuple       						= num_features[i]
				for j in range(backbone_config.depths[i]						):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						lowerCAmelCase  :     str       						= state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''						)
						lowerCAmelCase  :     Optional[Any]       						= state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''						)
						# next, add query, keys and values (in that order) to the state dict
						lowerCAmelCase  :     str       						= in_proj_weight[:dim, :]
						lowerCAmelCase  :     str       						= in_proj_bias[: dim]
						lowerCAmelCase  :     int       						= in_proj_weight[
						    dim : dim * 2, :
						]
						lowerCAmelCase  :     Tuple       						= in_proj_bias[
						    dim : dim * 2
						]
						lowerCAmelCase  :     int       						= in_proj_weight[
						    -dim :, :
						]
						lowerCAmelCase  :     int       						= in_proj_bias[-dim :]
						# fmt: on






def   __A    (				a_    :	Optional[int]   ,a_    :	List[Any]						):
		# transformer decoder self-attention layers
		lowerCAmelCase  :     str       						= config.d_model
		for i in range(config.decoder_layers						):
				# read in weights + bias of input projection layer of self-attention
				lowerCAmelCase  :     Tuple       						= state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''						)
				lowerCAmelCase  :     int       						= state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''						)
				# next, add query, keys and values (in that order) to the state dict
				lowerCAmelCase  :     List[Any]       						= in_proj_weight[:hidden_size, :]
				lowerCAmelCase  :     str       						= in_proj_bias[:hidden_size]
				lowerCAmelCase  :     Optional[int]       						= in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				lowerCAmelCase  :     int       						= in_proj_bias[hidden_size : hidden_size * 2]
				lowerCAmelCase  :     Dict       						= in_proj_weight[-hidden_size:, :]
				lowerCAmelCase  :     Dict       						= in_proj_bias[-hidden_size:]






def   __A    (				):
		lowerCAmelCase  :     List[str]       						= "http://images.cocodataset.org/val2017/000000039769.jpg"
		lowerCAmelCase  :     str       						= Image.open(requests.get(a_   ,stream=a_						).raw						)

		return im






@torch.no_grad()
def   __A    (				a_    :	Dict   ,a_    :	Tuple   ,a_    :	Optional[Any]						):
		lowerCAmelCase  :     str       						= get_deta_config(a_						)

		# load original state dict
		if model_name == "deta-swin-large":
				lowerCAmelCase  :     Optional[int]       						= hf_hub_download(repo_id="nielsr/deta-checkpoints"   ,filename="adet_swin_ft.pth"						)
		elif model_name == "deta-swin-large-o365":
				lowerCAmelCase  :     Union[str, Any]       						= hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"   ,filename="deta_swin_pt_o365.pth"						)
		else:
				raise ValueError(f'''Model name {model_name} not supported'''						)

		lowerCAmelCase  :     str       						= torch.load(a_   ,map_location="cpu"						)["model"]

		# original state dict
		for name, param in state_dict.items():
				print(a_   ,param.shape						)

		# rename keys
		lowerCAmelCase  :     List[str]       						= create_rename_keys(a_						)
		for src, dest in rename_keys:
				rename_key(a_   ,a_   ,a_						)
		read_in_swin_q_k_v(a_   ,config.backbone_config						)
		read_in_decoder_q_k_v(a_   ,a_						)

		# fix some prefixes
		for key in state_dict.copy().keys():
				if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
						lowerCAmelCase  :     int       						= state_dict.pop(a_						)
						lowerCAmelCase  :     Tuple       						= val
				if "input_proj" in key:
						lowerCAmelCase  :     str       						= state_dict.pop(a_						)
						lowerCAmelCase  :     Dict       						= val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						lowerCAmelCase  :     List[Any]       						= state_dict.pop(a_						)
						lowerCAmelCase  :     Dict       						= val

    # finally, create HuggingFace model and load state dict
		lowerCAmelCase  :     str       						= DetaForObjectDetection(a_						)
		model.load_state_dict(a_						)
		model.eval()

		lowerCAmelCase  :     List[Any]       						= "cuda" if torch.cuda.is_available() else "cpu"
		model.to(a_						)

		# load image processor
		lowerCAmelCase  :     List[str]       						= DetaImageProcessor(format="coco_detection"						)

		# verify our conversion on image
		lowerCAmelCase  :     Tuple       						= prepare_img()
		lowerCAmelCase  :     Optional[int]       						= processor(images=a_   ,return_tensors="pt"						)
		lowerCAmelCase  :     Any       						= encoding["pixel_values"]
		lowerCAmelCase  :     int       						= model(pixel_values.to(a_						)						)

		# verify logits
		print("Logits:"   ,outputs.logits[0, :3, :3]						)
		print("Boxes:"   ,outputs.pred_boxes[0, :3, :3]						)
		if model_name == "deta-swin-large":
				lowerCAmelCase  :     int       						= torch.tensor(
				    [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]						)
				lowerCAmelCase  :     Optional[int]       						= torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]						)
		elif model_name == "deta-swin-large-o365":
				lowerCAmelCase  :     Optional[Any]       						= torch.tensor(
				    [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]						)
				lowerCAmelCase  :     List[Any]       						= torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]						)
		assert torch.allclose(outputs.logits[0, :3, :3]   ,expected_logits.to(a_						)   ,atol=1e-4						)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,expected_boxes.to(a_						)   ,atol=1e-4						)
		print("Everything ok!"						)

		if pytorch_dump_folder_path:
				# Save model and processor
				logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''						)
				Path(a_						).mkdir(exist_ok=a_						)
				model.save_pretrained(a_						)
				processor.save_pretrained(a_						)

		# Push to hub
		if push_to_hub:
				print("Pushing model and processor to hub..."						)
				model.push_to_hub(f'''jozhang97/{model_name}'''						)
				processor.push_to_hub(f'''jozhang97/{model_name}'''						)


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()

					parser.add_argument(
					    """--model_name""",
					    type=str,
					    default="""deta-swin-large""",
					    choices=["""deta-swin-large""", """deta-swin-large-o365"""],
					    help="""Name of the model you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=str,
					    help="""Path to the folder to output PyTorch model.""",
					)
					parser.add_argument(
					    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
					)
					lowerCAmelCase									=   parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   []






def   __A    (				a_    :	list[list[int]]   ,a_    :	int   ,a_    :	int						):
		for i in range(len(a_						)						):
				if board[row][i] == 1:
						return False
		for i in range(len(a_						)						):
				if board[i][column] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,-1   ,-1						)						):
				if board[i][j] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,len(a_						)						)						):
				if board[i][j] == 1:
						return False
		return True






def   __A    (				a_    :	list[list[int]]   ,a_    :	int						):
		if row >= len(a_						):
				solution.append(a_						)
				printboard(a_						)
				print()
				return True
		for i in range(len(a_						)						):
				if is_safe(a_   ,a_   ,a_						):
						lowerCAmelCase  :     Dict       						= 1
						solve(a_   ,row + 1						)
						lowerCAmelCase  :     Optional[int]       						= 0
		return False






def   __A    (				a_    :	list[list[int]]						):
		for i in range(len(a_						)						):
				for j in range(len(a_						)						):
						if board[i][j] == 1:
								print("Q"   ,end=" "						)
						else:
								print("."   ,end=" "						)
				print()


# n=int(input("The no. of queens"))
lowerCAmelCase									=   8
lowerCAmelCase									=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("""The total no. of solutions are :""", len(solution))



